import gradio as gr from langchain_chroma import Chroma from pathlib import Path from utils import create_vector_db, Rag, get_chunks, get_conversation_chain, get_local_vector_db def chat(question, history) -> str: """ Get the chat data need for the gradio app :param question: The question being asked in the chat app. :type question: str :param history: A list of the conversation questions and answers. :type history: list :return: The answer from the current question. """ result = conversation_chain.invoke({"question": question}) answer = result['answer'] # include source documents if they exist # grab the first one as that should be related to the answer source_doc = "" if result.get('source_documents'): source_doc = result['source_documents'][0] response = f"{answer}\n\n**Source:**\n{source_doc.metadata.get('source', 'Source')}" \ if source_doc \ else answer return response def main(): gr.ChatInterface(chat, type="messages").launch(inbrowser=True) if __name__ == '__main__': create_new_db = False if Path('vector_db').exists() else True if create_new_db: folders = Path('knowledge_base').glob('*') chunks = get_chunks(folders=folders) vector_store = create_vector_db(chunks=chunks, db_name=Rag.DB_NAME.value, embeddings=Rag.EMBED_MODEL.value) else: client = get_local_vector_db(path='../rag_chat_example/vector_db') vector_store = Chroma(client=client, embedding_function=Rag.EMBED_MODEL.value) conversation_chain = get_conversation_chain(vectorstore=vector_store) main()